| Challenge: | Existing systems that generate scripts from a storyline are not available for this purpose. |
| Approach: | They propose a model that generates a story from a narrative and a tool that keeps track of what is said and what is to be said. |
| Outcome: | The proposed model outperforms baselines that use the narrative as a kind of context. |
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Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
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| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
| Outcome: | The proposed evaluation metrics and better datasets will improve narrative coherence and consistency and explore practical applications of story generation. |
Stylized Story Generation with Style-Guided Planning (2021.findings-acl)
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| Challenge: | Current storytelling systems focus more on generating stories with coherent plots regardless of the narration style. |
| Approach: | They propose a novel task, stylized story generation, that first plans stylized keywords and then generates the whole story with the guidance of the keywords. |
| Outcome: | The proposed model can generate emotion-driven or event-driven stories based on the ROCStories dataset . |
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning (2022.findings-emnlp)
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| Challenge: | Existing methods to automate story generation focus on single-character stories and lack basiccommonsense reasoning. |
| Approach: | They propose a commonsense-inference Augmentedneural StoryTelling framework that introduces commonsensical reasoning into the story generation process. |
| Outcome: | The proposed method produces significantly more coherent, on-topic, enjoyable andfluent stories than existing models in both the single-character and two-character settings. |
VScript: Controllable Script Generation with Visual Presentation (2022.aacl-demo)
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Ziwei Ji, Yan Xu, I-Tsun Cheng, Samuel Cahyawijaya, Rita Frieske, Etsuko Ishii, Min Zeng, Andrea Madotto, Pascale Fung
| Challenge: | Using a script generation system, scriptwriters can customize their scripts using video retrieval. |
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| Outcome: | The proposed system outperforms baselines on both automatic and human evaluations, especially in genre control. |
Hierarchical Neural Story Generation (P18-1)
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| Challenge: | a hierarchical model that generates a premise and then conditions on it creates fluent text . a novel form of model fusion improves the relevance of the story to the prompt . |
| Approach: | They use a hierarchical model that first generates a premise, then transforms it into a text . they use fusion to improve relevance of the story to the prompt and add a gated mechanism to model context . |
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STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)
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Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang
| Challenge: | Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience. |
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Cue Me In: Content-Inducing Approaches to Interactive Story Generation (2020.aacl-main)
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| Challenge: | Existing methods for automatic story generation focus on one-shot generation, but we focus on interactive story generation. |
| Approach: | They propose two ways to incorporate user-provided cue phrases into automatic story generation. |
| Outcome: | The proposed approach produces more topically coherent and personalized stories than baseline methods. |
Returning to the Start: Generating Narratives with Related Endpoints (2024.naacl-short)
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| Challenge: | RENarGen generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
| Approach: | They propose a novel novel novel that generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
| Outcome: | The proposed paradigm generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
Narrative Text Generation with a Latent Discrete Plan (2020.findings-emnlp)
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| Challenge: | Prior work on story generation has focused on generating consistent stories via story outlines using keywords or key phrases. |
| Approach: | They propose a deep latent variable model that first samples a sequence of anchor words, one per sentence in the story, as part of its generative process. |
| Outcome: | The proposed model gets favorable scores when evaluated on perplexity, diversity, and control of story via discrete plan. |
Planning Beyond Text: Graph-based Reasoning for Complex Narrative Generation (2026.findings-acl)
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| Challenge: | Existing methods for long-form complex narrative generation struggle to maintain global narrative coherence and logical consistency. |
| Approach: | They propose a framework that performs narrative planning on structural graph representations instead of direct sequential text representations. |
| Outcome: | The proposed model outperforms representative baselines across diverse scenarios. |